YAO/YAO-optimizer
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Newnius 2020-05-02 15:08:12 +08:00
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commit c9ff63fbbf
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train.py
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@ -108,7 +108,7 @@ def experiment(repeats, series, seed):
t2 = test.shape[0] % batch_size t2 = test.shape[0] % batch_size
train_trimmed = train_scaled[t1:, :] train_trimmed = train_scaled[t1:, :]
lstm_model = fit_lstm(train_trimmed, batch_size, 300, 4) lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4)
# forecast the entire training dataset to build up state for forecasting # forecast the entire training dataset to build up state for forecasting
print(train_trimmed) print(train_trimmed)
print(train_trimmed[:, 0]) print(train_trimmed[:, 0])